24 citations · 33 across the 6 of their papers we have counts for
6 papers
DVGBench: Implicit-to-Explicit Visual Grounding Benchmark in UAV Imagery with Large Vision-Language Models
Yue Zhou, Jue Chen, Zilun Zhang +10
Remote sensing (RS) large vision-language models (LVLMs) have shown strong promise across visual grounding (VG) tasks. However, existing RS VG datasets predominantly rely on explic…
InternVL-X: Advancing and Accelerating InternVL Series with Efficient Visual Token Compression
Dongchen Lu, Yuyao Sun, Zilu Zhang +4
Most multimodal large language models (MLLMs) treat visual tokens as "a sequence of text", integrating them with text tokens into a large language model (LLM). However, a great qua…
Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input
Zilun Zhang, Farzad Khalvati
Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding c…
DetailCLIP: Injecting Image Details into CLIP's Feature Space
Zilun Zhang, Cuifeng Shen, Yuan Shen +4
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224),…
Will Multi-modal Data Improves Few-shot Learning?
Zilun Zhang, Shihao Ma, Yichun Zhang
Most few-shot learning models utilize only one modality of data. We would like to investigate qualitatively and quantitatively how much will the model improve if we add an extra mo…
DPGN: Distribution Propagation Graph Network for Few-shot Learning
Ling Yang, Liangliang Li, Zilun Zhang +3
Most graph-network-based meta-learning approaches model instance-level relation of examples. We extend this idea further to explicitly model the distribution-level relation of one…